electricsheepafrica/africa-sudan-operational-presence

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- hxl
- operational-presence
- who-is-doing-what-and-where-3w-4w-5w
- sdn
pretty_name: "Sudan: Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 2135
- name: test
num_examples: 533
---

# Sudan: Operational Presence

**Publisher:** OCHA Sudan · **Source:** [HDX](https://data.humdata.org/dataset/sudan-operational-presence) · **License:** `cc-by` · **Updated:** 2026-04-01

---

## Abstract

The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working and what they are doing in order to identify gaps and plan for future humanitarian response. This dataset includes a list of humanitarian organizations by state and sector currently registered in Sudan. If you have updates for the 3W please contact [e-mail].

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `reporting_month` column(s). Geographic scope: **SDN**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 2,669 |
| **Columns** | 9 (0 numeric, 8 categorical, 1 datetime) |
| **Train split** | 2,135 rows |
| **Test split** | 533 rows |
| **Geographic scope** | SDN |
| **Publisher** | OCHA Sudan |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `state` (North Darfur, Khartoum, Gedaref), `locality` (Tawila, Wasat Al Gedaref, Madeinat Al Gedaref), `org_acronym` (SCI, UNICEF, RI), `org_type` (INGO, NNGO, UN Agency).

**Temporal** — `reporting_month`.

**Identifier / Metadata** — `cluster_name` (Protection, Health, Nutrition), `org_name` (Save the Children International, United Nations Children's Fund, Relief International), `esa_source` (HDX), `esa_processed` (2026-04-09).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sudan-operational-presence")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `reporting_month` | datetime64[ns] | 0.0% | |
| `state` | object | 0.0% | North Darfur, Khartoum, Gedaref |
| `locality` | object | 0.0% | Tawila, Wasat Al Gedaref, Madeinat Al Gedaref |
| `cluster_name` | object | 0.0% | Protection, Health, Nutrition |
| `org_name` | object | 0.0% | Save the Children International, United Nations Children's Fund, Relief International |
| `org_acronym` | object | 0.0% | SCI, UNICEF, RI |
| `org_type` | object | 0.0% | INGO, NNGO, UN Agency |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from OCHA Sudan and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/sudan-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_sudan_operational_presence,
title = {Sudan: Operational Presence},
author = {OCHA Sudan},
year = {2026},
url = {https://data.humdata.org/dataset/sudan-operational-presence},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*